STTR Phase I: Real-Time Mineral Data Collection and Identification Module

这个小型企业技术转让第一阶段项目通过消除处理体积庞大的样品来改善采矿和岩土工程钻探活动。 岩心钻探每天消耗约10，000加仑的水，通常在缺水地区。拟议的新方法消除了钻探过程中对水的需求，从而减轻了对公共和野生动物饮用水源的污染风险。将典型钻井项目所需的时间从数周减少到数天，减少了所需能源密集型钻井设备的碳排放。这种解决与传统技术获取地下数据相关的问题的解决方案具有210亿美元的可寻址细分市场价值。即将开发的新仪器将消除对场外实验室服务、岩心钻探和现场地质学家的需求，从而降低行业生命周期成本，并刺激美国所需战略矿产的关键勘探活动。该项目的智力价值是基于开发一种新型钻井模块，该模块将能够获得岩石和土壤的地下化学和岩土工程特性，以快速准确地评估存在的矿物的类型和价值。评估这些材料的传统方法是通过岩心钻探，这会带来与成本、样品质量和物流相关的各种问题。这项研究使用了光学图像，X射线荧光和伽马射线密度的组合，以及来自新策划的数据库的抗压强度数据，该数据库将从开源地理空间数据，地质和岩土工程报告的集合，岩芯数据库，隐式地质建模原理和机器学习方法开发，以表征岩石和土壤样本。它旨在解决目前阻止这种工具在当今行业中使用的技术挑战;这些挑战包括钻孔侧壁中的泥浆涂抹，目标环境的高压和高温以及无线数据传输的挑战。该研究的目标是通过硬件和软件创新的结合，创建、验证和校准一种方法，用于自动化表征未知岩石或土壤样品的原位特征。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

This Small Business Technology Transfer Phase I project improves mining and geotechnical drilling activities by eliminating handling of bulky samples. Core drilling consumes about 10,000 gallons of water per day, often in water-deficient areas. The proposed new approach negates the need for water in the drilling process, thereby mitigating the risk of pollution to public and wildlife drinking water sources. Reducing the time required for a typical drilling project from weeks to days reduces the carbon emission from the energy-intensive drilling equipment required. This solution to problems associated with acquiring subsurface data with traditional techniques has an addressable market segment value of $21 billion. The new instrument to be developed will eliminate the need for off-site lab services, core drilling, and site geologists, resulting in lower industry life cycle costs and spurring critical exploration activities for needed US strategic minerals. The intellectual merit of this project is based in the development of a novel drilling module that will be able to acquire subsurface chemistry and geotechnical properties of rocks and soils, to assess the type and value of minerals present both quickly and accurately. The conventional way of evaluating these materials is by core drilling, which presents various problems related to cost, sample quality and logistics. This research uses a combination of optical images, x-ray fluorescence, and gamma-ray density, as well as compressive strength data from a newly curated database that will be developed from open-source geospatial data, collections of geology and geotechnical reports, rock core databases, implicit geology modeling principles, and machine learning methods to characterize rock and soil samples. It aims to solve the technical challenges that currently prevent such a tool from being available to the industry today; these include mud smearing in the drill hole sidewalls, the high pressure and heat of the target environment, and challenges in wireless data transmission. The goal of this research is to create, validate and calibrate a method for automating the characterization of an unknown rock or soil sample in situ via a combination of hardware and software innovations.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.